Semantic Web for the Working Ontologist. Dean Allemang

mechanism (for example, Uniform Resource Identifier [URI]) to separate the naming from description and it must also allow for each of the differing viewpoints to be expressed.

       Variation and classes

      This problem is not a new one; it is a well-known problem in software engineering. When a software component is designed, it has to provide certain functionality, determined by information given to it at runtime. There is a trade-off in such a design; the component can be made to operate in a wide variety of circumstances, but it will require a complex input to describe just how it should behave at any one time. Or the system could be designed to work with very simple input but be useful in only a small number of very specific situations. The design of a software component inherently involves a model of the commonality and variability in the environment in which it is expected to be deployed. In response to this challenge, software methodology has developed the art of object modeling (in the context of Object-Oriented Programming, or OOP) as a means of organizing commonality and variability in software components.

      One of the primary organizing tools in OOP is the notion of a hierarchy of classes and subclasses. Classes high up in the hierarchy represent functionality that is common to a large number of components; classes farther down in a hierarchy represent more specific functionality. Commonality and variability in the functionality of a set of software components is represented in a class hierarchy.

      The Semantic Web standards also use this idea of class hierarchy for representing commonality and variability. Since the Semantic Web, unlike OOP, is not focused on software representation, classes are not defined in terms of behaviors of functions. But the notion of classes and subclasses remains, and it plays much the same role. High-level classes represent commonality among a large variety of entities, whereas lower-level classes represent commonality among a small, specific set of things.

      Let’s take Pluto as an example. The 2006 IAU definition of planet is quite specific in requiring these three criteria for a celestial body to be considered a planet:

      1. It is in orbit around the sun.

      2. It has sufficient mass to be nearly round.

      3. It has cleared the neighborhood around its orbit.

      The IAU goes further to state that a dwarf planet is a body that satisfies conditions 1 and 2 (and not 3); a body that satisfies only condition 1 is a small solar system body (SSSB). These definitions make a number of things clear: The classes SSSB, dwarf planet, and planet are all mutually exclusive; no celestial body is a member of any two classes. However, there is something that all of them have in common: They all are in orbit around the sun [Zielinski and Kumar 2006].

      Twentieth-century astronomy and astrology were not quite as organized as this; they didn’t have such rigorous definitions of the word planet. So how can we relate these notions to the twenty-first century notion of planet?

      The first thing we need is a way to talk about the various uses of the word planet: the IAU use, the astrological use, and the twentieth-century astronomical use. This seems like a simple requirement, but until it is met, we can’t even talk about the relationship among these terms. We will see details of the Semantic Web solution to this issue in Chapter 3, but for now, we will simply prefix each term with a short abbreviation of its source—for example, use IAU:Planet for the IAU use of the word, horo:Planet for the astrological use, and astro:Planet for the twentieth-century astronomical use.

      The solution begins by noticing what it is that all three notions of planet have in common; in this case, it is that the body orbits the sun. Thus, we can define a class of the things that orbit the sun, which we may as well call a solar system body, or SSB for short. All three notions are subclasses of this notion. This can be depicted graphically as in Figure 2.1.

      We can go further in this modeling when we observe that there are only eight IAU:Planets, and each one is also a horo:Planet and an astro:Planet. Thus, we can say that IAU:Planet is a subclass of both horo:Planet and astro:Planet, as shown in Figure 2.2. We can continue in this way, describing the relationships among all the concepts we have mentioned so far: IAU:DwarfPlanet and IAU:SSSB. As we go down the tree, each class refers to a more restrictive set of entities. In this way, we can model the commonality among entities (at a high level) while respecting their variation (at a low level).

       Variation and layers

      Classes and subclasses are a fine way to organize variation when there is a simple, known relationship between the modeled entities and it is possible to determine a clear ordering of classes that describes these relationships. In a Web setting, however, this usually is not the case. Each contributor can have something new to say that may fit in with previous statements in a wide variety of ways. How can we accommodate variation of sources if we can’t structure the entities they are describing into a class model?

      The Semantic Web provides an elegant solution to this problem. The basic idea is that any model can be built up from contributions from multiple sources. One way of thinking about this is to consider a model to be described in layers. Each layer comes from a different source. The entire model is the combination of all the layers, viewed as a single, unified whole.

      Figure 2.1 Subclass diagram for different notions of planet.

      Figure 2.2 More detailed relationships between various notions of planet.

      Let’s have a look at how this could work in the case of Pluto. Figure 2.3 illustrates how different communities could assert varying information about Pluto. In part (a) of the figure, we see some information about Pluto that is common among astrologers—namely, that Pluto signifies rebirth and regeneration and that the preferred symbol for referring to Pluto is the glyph indicated [Woolfolk 2012]. Part (b) shows some information that is of concern to astronomers, including the composition of the body Pluto and their preferred symbol. How can this variation be accommodated in a web of information? The simplest way is to simply merge the two models into a single one that includes all the information from each model, as shown in part (c).

      Merging models in this way is a conceptually simple thing to do, but how does it cope with variability? In the first place, it copes in the simplest way possible: It allows the astrologers and the astronomers to both have their say about Pluto (remember the AAA slogan!). For any party that is interested in both of these things (perhaps someone looking for a spiritual significance for elements?), the information can be viewed as a single, unified whole.

      Figure 2.3 Layers of modeled information about Pluto.

      But merging models in this way has a drawback as well. In Figure 2.3(c), there are two distinct glyphs, each claiming to be the “preferred” symbol for Pluto. This brings up issues of consistency of viewpoints. On the face of it, this appears to be an inconsistency because, from its name, we might expect that there can be exactly one preferred symbol (prefSymbol) for any SSB. But how can a machine know that? For a machine, the name prefSymbol can’t be treated any differently from any other label—for instance, madeOf or signifies. In such a context, how can we even tell that this is an inconsistency? After all, we don’t think it is an inconsistency that Pluto can be composed of more than one chemical compound or that it can signify more than one spiritual theme. Do we have to describe this in a natural language commentary on the model?

      Detailed answers to questions like these are exactly the reason why we need to publish models on the Semantic Web. When two (or more!) viewpoints